Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest China
Under the scenario of global climate warming, meteorological risks affecting sunflower cultivation in Xinjiang’s 10th Division were investigated by developing a meteorological-growth coupling model. Field experiments were conducted at three representative stations (A1–A3) during 2023–2024 to assess...
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MDPI AG
2025-07-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/7/1724 |
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| author | Jianguo Mu Jianqin Wang Ruiying Ma Zengshuai Lv Hongye Dong Yantao Liu Wei Duan Shengli Liu Peng Wang Xuekun Zhang |
| author_facet | Jianguo Mu Jianqin Wang Ruiying Ma Zengshuai Lv Hongye Dong Yantao Liu Wei Duan Shengli Liu Peng Wang Xuekun Zhang |
| author_sort | Jianguo Mu |
| collection | DOAJ |
| description | Under the scenario of global climate warming, meteorological risks affecting sunflower cultivation in Xinjiang’s 10th Division were investigated by developing a meteorological-growth coupling model. Field experiments were conducted at three representative stations (A1–A3) during 2023–2024 to assess temperature and precipitation impacts on yield and quality traits among sunflower cultivars with varying maturation periods. The main findings were: (1) Early-maturing cultivar B1 (RH3146) exhibited superior adaptation at low-temperature station A1, achieving 12% higher plant height and an 18% yield increase compared to regional averages. (2) At thermally variable station A2 (daily average temperature fluctuation ± 8 °C, precipitation CV = 25%), the late-maturing cultivar B3 showed enhanced stress resilience, achieving 35.6% grain crude fat content (15% greater than mid-maturing B2) along with 8–10% increases in seed setting rate and 100-grain weight. These improvements were potentially due to optimized photoassimilated allocation and activation of stress-responsive genes. (3) At station A3, characterized by high thermal-humidity variability (CV > 15%) during grain filling, B3 experienced a 15-day delay in maturation and a 3% reduction in ripeness. Two principal mitigation strategies are recommended: preferential selection of early-to-mid maturing cultivars in regions with thermal-humidity CV > 10%, improving yield stability by 23%, and optimization of sowing schedules based on accumulated temperature-precipitation modeling, reducing meteorological losses by 15%. These evidence-based recommendations provide critical insights for climate-resilient cultivar selection and precision agricultural management in meteorologically vulnerable agroecosystems. |
| format | Article |
| id | doaj-art-84ec41bfe5084c94a4bc80866b071b71 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-84ec41bfe5084c94a4bc80866b071b712025-08-20T03:35:36ZengMDPI AGAgronomy2073-43952025-07-01157172410.3390/agronomy15071724Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest ChinaJianguo Mu0Jianqin Wang1Ruiying Ma2Zengshuai Lv3Hongye Dong4Yantao Liu5Wei Duan6Shengli Liu7Peng Wang8Xuekun Zhang9College of Agriculture, Tarim University, Alar 843300, ChinaCrop Research Institute of Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, ChinaCollege of Agriculture, Tarim University, Alar 843300, ChinaCrop Research Institute of Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, ChinaCrop Research Institute of Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, ChinaCrop Research Institute of Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, ChinaCrop Research Institute of Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, ChinaCrop Research Institute of Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, ChinaCrop Research Institute of Xinjiang Academy of Agricultural Reclamation Sciences, Shihezi 832000, ChinaCollege of Agriculture/Key Laboratory of Oasis Agricultural Pest Management and Plant Protection Resources Utilization, Xinjiang Uygur Autonomous Region, Shihezi University, Shihezi 832003, ChinaUnder the scenario of global climate warming, meteorological risks affecting sunflower cultivation in Xinjiang’s 10th Division were investigated by developing a meteorological-growth coupling model. Field experiments were conducted at three representative stations (A1–A3) during 2023–2024 to assess temperature and precipitation impacts on yield and quality traits among sunflower cultivars with varying maturation periods. The main findings were: (1) Early-maturing cultivar B1 (RH3146) exhibited superior adaptation at low-temperature station A1, achieving 12% higher plant height and an 18% yield increase compared to regional averages. (2) At thermally variable station A2 (daily average temperature fluctuation ± 8 °C, precipitation CV = 25%), the late-maturing cultivar B3 showed enhanced stress resilience, achieving 35.6% grain crude fat content (15% greater than mid-maturing B2) along with 8–10% increases in seed setting rate and 100-grain weight. These improvements were potentially due to optimized photoassimilated allocation and activation of stress-responsive genes. (3) At station A3, characterized by high thermal-humidity variability (CV > 15%) during grain filling, B3 experienced a 15-day delay in maturation and a 3% reduction in ripeness. Two principal mitigation strategies are recommended: preferential selection of early-to-mid maturing cultivars in regions with thermal-humidity CV > 10%, improving yield stability by 23%, and optimization of sowing schedules based on accumulated temperature-precipitation modeling, reducing meteorological losses by 15%. These evidence-based recommendations provide critical insights for climate-resilient cultivar selection and precision agricultural management in meteorologically vulnerable agroecosystems.https://www.mdpi.com/2073-4395/15/7/1724sunflower growth periodmeteorological sensitivitygrain quality |
| spellingShingle | Jianguo Mu Jianqin Wang Ruiying Ma Zengshuai Lv Hongye Dong Yantao Liu Wei Duan Shengli Liu Peng Wang Xuekun Zhang Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest China Agronomy sunflower growth period meteorological sensitivity grain quality |
| title | Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest China |
| title_full | Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest China |
| title_fullStr | Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest China |
| title_full_unstemmed | Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest China |
| title_short | Optimizing Sunflower Cultivar Selection Under Climate Variability: Evidence from Coupled Meteorological-Growth Modeling in Arid Northwest China |
| title_sort | optimizing sunflower cultivar selection under climate variability evidence from coupled meteorological growth modeling in arid northwest china |
| topic | sunflower growth period meteorological sensitivity grain quality |
| url | https://www.mdpi.com/2073-4395/15/7/1724 |
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